Employee Promotion Analysis

This employee promotion data tells about the specifications of employees who get promotions and those who don't.

The information of the dataset:

• employee_id : Unique ID for employee.
• department : Department of employee
• region : Region of employment (unordered)
• education : Education Level.
• gender : Gender of Employee.
• recruitment_channel : Channel of recruitment for employees.
• no_of_trainings : no. of other training completed in previous year on soft skills, technical skills etc.
• date_of_birth: employee date of birth
• age : Age of Employee.
• join_date: Employee join date
• previous_year_rating : Employee Rating for the previous year.
• length_of_service : Length of service in years.
• KPIs_met >80% : If Percent of KPIs(Key performance Indicators) >80% then 1 else 0.
• awards_won? : If awards won during the previous year then 1 else 0.
• avg_training_score : Average score in current training evaluations.
• is_promoted : Recommended for promotion.

Import Libraries

from dash import Dash, html, dcc, Input, Output
import plotly.express as px
import pandas as pd
import dash_bootstrap_components as dbc

Read Dataset

promotion = pd.read_csv('promotion_clean.csv')

Cleansing Data

promotion[['department','region','education',
           'gender','recruitment_channel',
           'KPIs_met >80%','awards_won?',
           'is_promoted']] = promotion[['department','region',
                                        'education','gender',
                                        'recruitment_channel',
                                        'KPIs_met >80%','awards_won?',
                                        'is_promoted']].astype('category')

promotion[['date_of_birth','join_date']] = promotion[['date_of_birth','join_date']].astype('datetime64')

Exploratory Data Analysis

This is the information of employeed in our Start-Up. help to identify who is a potential candidate for promotion

Total employees

promotion.shape[0]

54808

Number of employees promoted

promotion[promotion['is_promoted']=='Yes'].shape[0]

4668

Breaking Down a Ranking: Each Department

Number of employee in each department each promotion status

data_agg = promotion.groupby(['department','is_promoted']).count()[['employee_id']].reset_index()
data_agg = data_agg.sort_values(by = 'employee_id')
bar_plot1 = px.bar(
    data_agg,
    x = 'employee_id',
    y = 'department',
    color = 'is_promoted',
    color_discrete_sequence = ['#618685','#80ced6'],
    barmode = 'group',
    orientation='h',
    template = 'ggplot2',
    labels = {
        'department': 'Department',
        'employee_id': 'No of Employee',
        'is_promoted': 'Is Promoted?',
    },
    title = 'Number of employees in each department',
    height=700,
).update_layout(showlegend=False)

bar_plot1

We gain the insight that Sales & Marketing department have the highest proportion of promotion relative to the number of employee inside that department

New Hires

data_2020 = promotion[promotion['join_date'] >= '2020-01-01']
data_2020 = data_2020.groupby(['join_date']).count()['employee_id'].reset_index().tail(30)
line_plot2 = px.line(
    data_2020,
    x='join_date',
    y='employee_id',
    markers=True,
    color_discrete_sequence = ['#618685'],
    template = 'ggplot2',
    labels={
        'join_date':'Join date',
        'employee_id':'Number of employee'
    },
    title = 'Number of new hires in the last 30 days',
    height=700,
)

line_plot2

Length of Service Distribution

promotion['department'].unique()

['Sales & Marketing', 'Operations', 'Technology', 'Analytics', 'R&D', 'Procurement', 'Finance', 'HR', 'Legal']
Categories (9, object): ['Analytics', 'Finance', 'HR', 'Legal', ..., 'Procurement', 'R&D', 'Sales & Marketing', 'Technology']

In Technology

data_agg = promotion[promotion['department'] == 'Technology']
hist_plot3 = px.histogram(
    data_agg,
    x = 'length_of_service',
    nbins = 20,
    color_discrete_sequence = ['#618685','#80ced6'],
    title = 'Length of Service Distribution in Technology Department',
    template = 'ggplot2',
    labels={
        'length_of_service': 'Length of Service (years)',
    },
    marginal = 'box',
    height=700,
)

hist_plot3

Layout